Public health monitoring of behavioural risk factors in USA: An exploratory study

Abstract

Abstract Background The COVID-19 pandemic's restrictions had a significant impact on behavioural markers such as physical, sedentary and sleep activity. The Behavioral Risk Factor Surveillance System is a key data source for public health surveillance in the USA but is limited by subjectivity and data quality, and new-generation data sources like Fitbits face challenges with battery life and data access. This research study aims to use zero-effort technology and IoT-based big data to evaluate the impact of the COVID-19 pandemic on population-level behavioural changes in USA. Methods The study used a proposed methodology to analyze data from 470 households in New Mexico, USA, using the DYD dataset from Donate your Data initiative by ecobee (a smart thermostat company). The Microsoft Azure data lake is used for the storage of raw data and the Azure databricks is used for data pre-processing, processing, and analysis. The Gaussian mixture model is used to identify sleep parameters by segmenting the sleep cycle records into different clusters. The quantity of sleep is measured by a motion sensor based on the absence of movement, and an increase in sensor activation indicates longer duration of household occupancy. Results The findings show significant changes at the household and population level for the selected behavioural health indicators (sleep-time, wake-up time, time spent indoors, time spent outdoors) during COVID-19 pandemic, which could be attributed to the policy changes implemented to curb the spread of COVID-19. The study findings are shown by 1) heatmap visualizations at the household level showing trends for the selected indicators during the Covid-19 pandemic; and 2) statistical analysis indicating a significant difference in selected behavioural health indicators before and during the pandemic. Conclusions These innovative data analytics have the potential to provide real-time insights and alert system activation to monitor, promote, and improve public health. Key messages • Sleep health analysis using IoT data is a novel method of measuring public health indicators objectively using zero effort technology. • The available evidence from this study can further offer surveillance systems with near-real-time behavioral markers; alert system activation; and measure short- and long-term impact policy changes.